SPL03: WaveWise: Harnessing data for ocean health Building a machine learning model that analyzes underwater drone footage to detect coral bleaching, marine litter, or endangered species, automating ocean health assessments.

Our oceans are changing — fast. Coral reefs are bleaching, marine litter is piling up, and endangered species are becoming harder to track. What if we could turn hours of underwater footage into actionable insights, automatically?  WaveWise is a challenge to build an AI-powered solution that analyzes underwater drone footage to detect signs of coral bleaching, plastic pollution, and the presence of endangered marine life — all in real time. Using machine learning and computer vision, we aim to automate what currently takes teams of scientists countless hours to do manually.  Imagine a tool that empowers researchers, NGOs, and even citizen scientists to monitor the health of our oceans using just video input — with AI doing the heavy lifting. We’ll train models on real-world ocean footage, create a simple interface for uploading and analyzing data, and visualize results with clean dashboards.  If you’re passionate about the environment, excited by the power of AI, or love building practical tools with real-world impact, join us. Whether you're a data scientist, marine biologist, designer, or dev — we need your brain to help the ocean breathe.  Let’s make the ocean speak — in data.

Over the 48 hours, I want to develop an AI-powered digital tool that analyzes underwater drone footage to automatically detect coral bleaching, marine litter, and endangered species using computer vision.

This tool will include:

A trained machine learning model (object detection/classification)

A simple web-based interface where users can upload images or video clips

A visual dashboard showing analysis results (e.g., detections, locations, health stats)

The end goal is to create a functional prototype that demonstrates how automation and data analysis can transform ocean health monitoring — making it faster, scalable, and more accessible.

UX/UI Designer – to make the tool intuitive and engaging for non-technical users

Web Developer – to create the user interface and visualization dashboard

Machine Learning / AI Specialist – to build and train the object detection models